MobileNet V2深入理解

转载：https://zhuanlan.zhihu.com/p/33075914 MobileNet V2 论文初读
转载：https://blog.csdn.net/wfei101/article/details/79334659 网络模型压缩和优化:MobileNet V2网络结构理解
转载： https://zhuanlan.zhihu.com/p/50045821 mobilenetv1和mobilenetv2的区别
MobileNetV2: Inverted Residuals and Linear Bottlenecks：连接：https://128.84.21.199/pdf/1801.04381.pdf
MobileNet v1中使用的Depthwise Separable Convolution是模型压缩的一个最为经典的策略，它是通过将跨通道的 $3\times3$ 卷积换成单通道的 $3\times3$ 卷积+跨通道的 $1\times1$ 卷积来达到此目的的。
MobileNet V2主要的改进有两点：
1、Linear Bottlenecks。因为ReLU的在通道数较少的Feature Map上有非常严重信息损失问题，所以去掉了小维度输出层后面的非线性激活层ReLU，保留更多的特征信息，目的是为了保证模型的表达能力。
2、Inverted Residual block。该结构和传统residual block中维度先缩减再扩增正好相反，因此shotcut也就变成了连接的是维度缩减后的feature map。
相同点：
都采用 Depth-wise (DW) 卷积搭配 Point-wise (PW) 卷积的方式来提特征。这两个操作合起来也被称为 Depth-wise Separable Convolution，之前在 Xception 中被广泛使用。这么做的好处是理论上可以成倍的减少卷积层的时间复杂度和空间复杂度。由下式可知，因为卷积核的尺寸 $K$ 通常远小于输出通道数 $C_{out}$ ，因此标准卷积的计算复杂度近似为 DW + PW 组合卷积的 $K^2$ 倍。由于Depthwise卷积的每个通道Feature Map产生且仅产生一个与之对应的Feature Map，也就是说输出层的Feature Map的channel数量等于输入层的Feature map的数量。因此DepthwiseConv不需要控制输出层的Feature Map的数量，因此并没有num_filters 这个参数，这个参数是和输入特征的channels数相等。
standard Convolution运算量：3*3跨通道运算 C*（C*（K**2)*x），其中x为一个kernel核在一个一维的输入特征上运算需要滑动的次数，这里假设卷积核个数和输入通道数都是C；
Depth-wise Separable Convolution运算量：单通道运算（C*（K**2)*x）+ 跨通道1*1卷积 C*(C*(1**2)*x)，，其中x为一个kernel核在一个一维的输入特征上运算需要滑动的次数，这里假设卷积核个数和输入通道数都是C；
$\quad\quad\displaystyle\textrm{Complexity} \quad\quad \frac{\textbf{Depth-wise Separable CONV}}{\textbf{Standard CONV}} = \frac{1}{K^2} + \frac{1}{C_{out}} \sim \frac{1}{K^2}$
Depthwise卷积示意图（3个通道）
主要创新点：
1，Inverted residuals：V2 在 DW 卷积之前新加了一个 1*1 大小PW 卷积。这么做的原因，是因为 DW 卷积由于本身的计算特性决定它自己没有改变通道数的能力，上一层给它多少通道，它就只能输出多少通道。所以如果上一层给的通道数本身很少的话，DW 也只能很委屈的在低维空间提特征，因此效果不够好。现在 V2 为了改善这个问题，给每个 DW 之前都配备了一个 PW，专门用来升维，定义升维系数 t（而在v2中这个值一般是介于 $5-10$ 之间的数，在作者的实验中， $t=6$ ），这样不管输入通道数 $C_{in}$ 是多是少，经过第一个 PW 升维之后，DW 都是在相对的更高维 ( $t \cdot C_{in}$ ) 进行着辛勤工作的。主要也是为了提取更多的通道信息，得到更多的特征线信息。
2，Linear bottlenecks：V2 去掉了第二个 PW 的激活函数，意思就是bottleneck的输出不接非线性激活层。论文作者称其为 Linear Bottleneck。这么做的原因，是因为作者认为激活函数在高维空间能够有效的增加非线性，而在低维空间时则会破坏特征，不如线性的效果好。由于第二个 PW 的主要功能就是降维，因此按照上面的理论，降维之后就不宜再使用 ReLU6 了。
再看看MobileNetV2的block 与ResNet 的block：主要不同之处就在于，ResNet是：压缩”→“卷积提特征”→“扩张”，MobileNetV2则是Inverted residuals, 即：“扩张”→“卷积提特征”→ “压缩
具体mobilenetV2的宏观结构如下：t表示每个bottleneck的PW层的expand系数，也就是channels扩张系数，
c表示每个bottleneck的输出通道数，也就是每个bottleneck输出的PW的channels数，用于降维，
n表示有多少个bottleneck连接在一起，s表示第一个bottleneck的DW层的stride，表示下采样；
附上mobilenetv2的源码，可以通过netscope: https://ethereon.github.io/netscope/#/editor查看：
name: "MOBILENET_V2"
layer {
    name: "data"
    type: "ImageData"
    top: "data"
    top: "label"
    include {
        phase: TRAIN
    }
    transform_param {
        mirror: true
        crop_size: 224
    }
    image_data_param {
        source: "./train.txt"
        batch_size: 24
        shuffle: false
    }
}
layer {
    name: "data"
    type: "ImageData"
    top: "data"
    top: "label"
    include {
        phase: TEST
    }
    transform_param {
        mirror: false
        crop_size: 224
    }
    image_data_param {
        source: "./valid.txt"
        batch_size: 16
    }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1/bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv1/scale"
  type: "Scale"
  bottom: "conv1/bn"
  top: "conv1/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1/bn"
  top: "conv1/bn"
}
layer {
  name: "conv2_1/expand"
  type: "Convolution"
  bottom: "conv1/bn"
  top: "conv2_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv2_1/expand"
  top: "conv2_1/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_1/expand/scale"
  type: "Scale"
  bottom: "conv2_1/expand/bn"
  top: "conv2_1/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu2_1/expand"
  type: "ReLU"
  bottom: "conv2_1/expand/bn"
  top: "conv2_1/expand/bn"
}
layer {
  name: "conv2_1/dwise"
  type: "Convolution"
  bottom: "conv2_1/expand/bn"
  top: "conv2_1/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv2_1/dwise/bn"
  type: "BatchNorm"
  bottom: "conv2_1/dwise"
  top: "conv2_1/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_1/dwise/scale"
  type: "Scale"
  bottom: "conv2_1/dwise/bn"
  top: "conv2_1/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu2_1/dwise"
  type: "ReLU"
  bottom: "conv2_1/dwise/bn"
  top: "conv2_1/dwise/bn"
}
layer {
  name: "conv2_1/linear"
  type: "Convolution"
  bottom: "conv2_1/dwise/bn"
  top: "conv2_1/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 16
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1/linear/bn"
  type: "BatchNorm"
  bottom: "conv2_1/linear"
  top: "conv2_1/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_1/linear/scale"
  type: "Scale"
  bottom: "conv2_1/linear/bn"
  top: "conv2_1/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv2_2/expand"
  type: "Convolution"
  bottom: "conv2_1/linear/bn"
  top: "conv2_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv2_2/expand"
  top: "conv2_2/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_2/expand/scale"
  type: "Scale"
  bottom: "conv2_2/expand/bn"
  top: "conv2_2/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu2_2/expand"
  type: "ReLU"
  bottom: "conv2_2/expand/bn"
  top: "conv2_2/expand/bn"
}
layer {
  name: "conv2_2/dwise"
  type: "Convolution"
  bottom: "conv2_2/expand/bn"
  top: "conv2_2/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 96
    stride: 2
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv2_2/dwise/bn"
  type: "BatchNorm"
  bottom: "conv2_2/dwise"
  top: "conv2_2/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_2/dwise/scale"
  type: "Scale"
  bottom: "conv2_2/dwise/bn"
  top: "conv2_2/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu2_2/dwise"
  type: "ReLU"
  bottom: "conv2_2/dwise/bn"
  top: "conv2_2/dwise/bn"
}
layer {
  name: "conv2_2/linear"
  type: "Convolution"
  bottom: "conv2_2/dwise/bn"
  top: "conv2_2/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 24
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2/linear/bn"
  type: "BatchNorm"
  bottom: "conv2_2/linear"
  top: "conv2_2/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv2_2/linear/scale"
  type: "Scale"
  bottom: "conv2_2/linear/bn"
  top: "conv2_2/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv3_1/expand"
  type: "Convolution"
  bottom: "conv2_2/linear/bn"
  top: "conv3_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv3_1/expand"
  top: "conv3_1/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_1/expand/scale"
  type: "Scale"
  bottom: "conv3_1/expand/bn"
  top: "conv3_1/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu3_1/expand"
  type: "ReLU"
  bottom: "conv3_1/expand/bn"
  top: "conv3_1/expand/bn"
}
layer {
  name: "conv3_1/dwise"
  type: "Convolution"
  bottom: "conv3_1/expand/bn"
  top: "conv3_1/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 144
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv3_1/dwise/bn"
  type: "BatchNorm"
  bottom: "conv3_1/dwise"
  top: "conv3_1/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_1/dwise/scale"
  type: "Scale"
  bottom: "conv3_1/dwise/bn"
  top: "conv3_1/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu3_1/dwise"
  type: "ReLU"
  bottom: "conv3_1/dwise/bn"
  top: "conv3_1/dwise/bn"
}
layer {
  name: "conv3_1/linear"
  type: "Convolution"
  bottom: "conv3_1/dwise/bn"
  top: "conv3_1/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 24
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1/linear/bn"
  type: "BatchNorm"
  bottom: "conv3_1/linear"
  top: "conv3_1/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_1/linear/scale"
  type: "Scale"
  bottom: "conv3_1/linear/bn"
  top: "conv3_1/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_3_1"
  type: "Eltwise"
  bottom: "conv2_2/linear/bn"
  bottom: "conv3_1/linear/bn"
  top: "block_3_1"
}
layer {
  name: "conv3_2/expand"
  type: "Convolution"
  bottom: "block_3_1"
  top: "conv3_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv3_2/expand"
  top: "conv3_2/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_2/expand/scale"
  type: "Scale"
  bottom: "conv3_2/expand/bn"
  top: "conv3_2/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu3_2/expand"
  type: "ReLU"
  bottom: "conv3_2/expand/bn"
  top: "conv3_2/expand/bn"
}
layer {
  name: "conv3_2/dwise"
  type: "Convolution"
  bottom: "conv3_2/expand/bn"
  top: "conv3_2/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 144
    stride: 2
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv3_2/dwise/bn"
  type: "BatchNorm"
  bottom: "conv3_2/dwise"
  top: "conv3_2/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_2/dwise/scale"
  type: "Scale"
  bottom: "conv3_2/dwise/bn"
  top: "conv3_2/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu3_2/dwise"
  type: "ReLU"
  bottom: "conv3_2/dwise/bn"
  top: "conv3_2/dwise/bn"
}
layer {
  name: "conv3_2/linear"
  type: "Convolution"
  bottom: "conv3_2/dwise/bn"
  top: "conv3_2/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2/linear/bn"
  type: "BatchNorm"
  bottom: "conv3_2/linear"
  top: "conv3_2/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv3_2/linear/scale"
  type: "Scale"
  bottom: "conv3_2/linear/bn"
  top: "conv3_2/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv4_1/expand"
  type: "Convolution"
  bottom: "conv3_2/linear/bn"
  top: "conv4_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_1/expand"
  top: "conv4_1/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_1/expand/scale"
  type: "Scale"
  bottom: "conv4_1/expand/bn"
  top: "conv4_1/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_1/expand"
  type: "ReLU"
  bottom: "conv4_1/expand/bn"
  top: "conv4_1/expand/bn"
}
layer {
  name: "conv4_1/dwise"
  type: "Convolution"
  bottom: "conv4_1/expand/bn"
  top: "conv4_1/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 192
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_1/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_1/dwise"
  top: "conv4_1/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_1/dwise/scale"
  type: "Scale"
  bottom: "conv4_1/dwise/bn"
  top: "conv4_1/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_1/dwise"
  type: "ReLU"
  bottom: "conv4_1/dwise/bn"
  top: "conv4_1/dwise/bn"
}
layer {
  name: "conv4_1/linear"
  type: "Convolution"
  bottom: "conv4_1/dwise/bn"
  top: "conv4_1/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_1/linear"
  top: "conv4_1/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_1/linear/scale"
  type: "Scale"
  bottom: "conv4_1/linear/bn"
  top: "conv4_1/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_4_1"
  type: "Eltwise"
  bottom: "conv3_2/linear/bn"
  bottom: "conv4_1/linear/bn"
  top: "block_4_1"
}
layer {
  name: "conv4_2/expand"
  type: "Convolution"
  bottom: "block_4_1"
  top: "conv4_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_2/expand"
  top: "conv4_2/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_2/expand/scale"
  type: "Scale"
  bottom: "conv4_2/expand/bn"
  top: "conv4_2/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_2/expand"
  type: "ReLU"
  bottom: "conv4_2/expand/bn"
  top: "conv4_2/expand/bn"
}
layer {
  name: "conv4_2/dwise"
  type: "Convolution"
  bottom: "conv4_2/expand/bn"
  top: "conv4_2/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 192
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_2/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_2/dwise"
  top: "conv4_2/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_2/dwise/scale"
  type: "Scale"
  bottom: "conv4_2/dwise/bn"
  top: "conv4_2/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_2/dwise"
  type: "ReLU"
  bottom: "conv4_2/dwise/bn"
  top: "conv4_2/dwise/bn"
}
layer {
  name: "conv4_2/linear"
  type: "Convolution"
  bottom: "conv4_2/dwise/bn"
  top: "conv4_2/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_2/linear"
  top: "conv4_2/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_2/linear/scale"
  type: "Scale"
  bottom: "conv4_2/linear/bn"
  top: "conv4_2/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_4_2"
  type: "Eltwise"
  bottom: "block_4_1"
  bottom: "conv4_2/linear/bn"
  top: "block_4_2"
}
layer {
  name: "conv4_3/expand"
  type: "Convolution"
  bottom: "block_4_2"
  top: "conv4_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_3/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_3/expand"
  top: "conv4_3/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_3/expand/scale"
  type: "Scale"
  bottom: "conv4_3/expand/bn"
  top: "conv4_3/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_3/expand"
  type: "ReLU"
  bottom: "conv4_3/expand/bn"
  top: "conv4_3/expand/bn"
}
layer {
  name: "conv4_3/dwise"
  type: "Convolution"
  bottom: "conv4_3/expand/bn"
  top: "conv4_3/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 192
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_3/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_3/dwise"
  top: "conv4_3/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_3/dwise/scale"
  type: "Scale"
  bottom: "conv4_3/dwise/bn"
  top: "conv4_3/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_3/dwise"
  type: "ReLU"
  bottom: "conv4_3/dwise/bn"
  top: "conv4_3/dwise/bn"
}
layer {
  name: "conv4_3/linear"
  type: "Convolution"
  bottom: "conv4_3/dwise/bn"
  top: "conv4_3/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_3/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_3/linear"
  top: "conv4_3/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_3/linear/scale"
  type: "Scale"
  bottom: "conv4_3/linear/bn"
  top: "conv4_3/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv4_4/expand"
  type: "Convolution"
  bottom: "conv4_3/linear/bn"
  top: "conv4_4/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_4/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_4/expand"
  top: "conv4_4/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_4/expand/scale"
  type: "Scale"
  bottom: "conv4_4/expand/bn"
  top: "conv4_4/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_4/expand"
  type: "ReLU"
  bottom: "conv4_4/expand/bn"
  top: "conv4_4/expand/bn"
}
layer {
  name: "conv4_4/dwise"
  type: "Convolution"
  bottom: "conv4_4/expand/bn"
  top: "conv4_4/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_4/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_4/dwise"
  top: "conv4_4/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_4/dwise/scale"
  type: "Scale"
  bottom: "conv4_4/dwise/bn"
  top: "conv4_4/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_4/dwise"
  type: "ReLU"
  bottom: "conv4_4/dwise/bn"
  top: "conv4_4/dwise/bn"
}
layer {
  name: "conv4_4/linear"
  type: "Convolution"
  bottom: "conv4_4/dwise/bn"
  top: "conv4_4/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_4/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_4/linear"
  top: "conv4_4/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_4/linear/scale"
  type: "Scale"
  bottom: "conv4_4/linear/bn"
  top: "conv4_4/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_4_4"
  type: "Eltwise"
  bottom: "conv4_3/linear/bn"
  bottom: "conv4_4/linear/bn"
  top: "block_4_4"
}
layer {
  name: "conv4_5/expand"
  type: "Convolution"
  bottom: "block_4_4"
  top: "conv4_5/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_5/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_5/expand"
  top: "conv4_5/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_5/expand/scale"
  type: "Scale"
  bottom: "conv4_5/expand/bn"
  top: "conv4_5/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_5/expand"
  type: "ReLU"
  bottom: "conv4_5/expand/bn"
  top: "conv4_5/expand/bn"
}
layer {
  name: "conv4_5/dwise"
  type: "Convolution"
  bottom: "conv4_5/expand/bn"
  top: "conv4_5/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_5/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_5/dwise"
  top: "conv4_5/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_5/dwise/scale"
  type: "Scale"
  bottom: "conv4_5/dwise/bn"
  top: "conv4_5/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_5/dwise"
  type: "ReLU"
  bottom: "conv4_5/dwise/bn"
  top: "conv4_5/dwise/bn"
}
layer {
  name: "conv4_5/linear"
  type: "Convolution"
  bottom: "conv4_5/dwise/bn"
  top: "conv4_5/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_5/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_5/linear"
  top: "conv4_5/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_5/linear/scale"
  type: "Scale"
  bottom: "conv4_5/linear/bn"
  top: "conv4_5/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_4_5"
  type: "Eltwise"
  bottom: "block_4_4"
  bottom: "conv4_5/linear/bn"
  top: "block_4_5"
}
layer {
  name: "conv4_6/expand"
  type: "Convolution"
  bottom: "block_4_5"
  top: "conv4_6/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_6/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_6/expand"
  top: "conv4_6/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_6/expand/scale"
  type: "Scale"
  bottom: "conv4_6/expand/bn"
  top: "conv4_6/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_6/expand"
  type: "ReLU"
  bottom: "conv4_6/expand/bn"
  top: "conv4_6/expand/bn"
}
layer {
  name: "conv4_6/dwise"
  type: "Convolution"
  bottom: "conv4_6/expand/bn"
  top: "conv4_6/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_6/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_6/dwise"
  top: "conv4_6/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_6/dwise/scale"
  type: "Scale"
  bottom: "conv4_6/dwise/bn"
  top: "conv4_6/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_6/dwise"
  type: "ReLU"
  bottom: "conv4_6/dwise/bn"
  top: "conv4_6/dwise/bn"
}
layer {
  name: "conv4_6/linear"
  type: "Convolution"
  bottom: "conv4_6/dwise/bn"
  top: "conv4_6/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_6/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_6/linear"
  top: "conv4_6/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_6/linear/scale"
  type: "Scale"
  bottom: "conv4_6/linear/bn"
  top: "conv4_6/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_4_6"
  type: "Eltwise"
  bottom: "block_4_5"
  bottom: "conv4_6/linear/bn"
  top: "block_4_6"
}
layer {
  name: "conv4_7/expand"
  type: "Convolution"
  bottom: "block_4_6"
  top: "conv4_7/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_7/expand/bn"
  type: "BatchNorm"
  bottom: "conv4_7/expand"
  top: "conv4_7/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_7/expand/scale"
  type: "Scale"
  bottom: "conv4_7/expand/bn"
  top: "conv4_7/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu4_7/expand"
  type: "ReLU"
  bottom: "conv4_7/expand/bn"
  top: "conv4_7/expand/bn"
}
layer {
  name: "conv4_7/dwise"
  type: "Convolution"
  bottom: "conv4_7/expand/bn"
  top: "conv4_7/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    stride: 2
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv4_7/dwise/bn"
  type: "BatchNorm"
  bottom: "conv4_7/dwise"
  top: "conv4_7/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_7/dwise/scale"
  type: "Scale"
  bottom: "conv4_7/dwise/bn"
  top: "conv4_7/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu4_7/dwise"
  type: "ReLU"
  bottom: "conv4_7/dwise/bn"
  top: "conv4_7/dwise/bn"
}
layer {
  name: "conv4_7/linear"
  type: "Convolution"
  bottom: "conv4_7/dwise/bn"
  top: "conv4_7/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_7/linear/bn"
  type: "BatchNorm"
  bottom: "conv4_7/linear"
  top: "conv4_7/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv4_7/linear/scale"
  type: "Scale"
  bottom: "conv4_7/linear/bn"
  top: "conv4_7/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv5_1/expand"
  type: "Convolution"
  bottom: "conv4_7/linear/bn"
  top: "conv5_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv5_1/expand"
  top: "conv5_1/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_1/expand/scale"
  type: "Scale"
  bottom: "conv5_1/expand/bn"
  top: "conv5_1/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu5_1/expand"
  type: "ReLU"
  bottom: "conv5_1/expand/bn"
  top: "conv5_1/expand/bn"
}
layer {
  name: "conv5_1/dwise"
  type: "Convolution"
  bottom: "conv5_1/expand/bn"
  top: "conv5_1/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv5_1/dwise/bn"
  type: "BatchNorm"
  bottom: "conv5_1/dwise"
  top: "conv5_1/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_1/dwise/scale"
  type: "Scale"
  bottom: "conv5_1/dwise/bn"
  top: "conv5_1/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu5_1/dwise"
  type: "ReLU"
  bottom: "conv5_1/dwise/bn"
  top: "conv5_1/dwise/bn"
}
layer {
  name: "conv5_1/linear"
  type: "Convolution"
  bottom: "conv5_1/dwise/bn"
  top: "conv5_1/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1/linear/bn"
  type: "BatchNorm"
  bottom: "conv5_1/linear"
  top: "conv5_1/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_1/linear/scale"
  type: "Scale"
  bottom: "conv5_1/linear/bn"
  top: "conv5_1/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_5_1"
  type: "Eltwise"
  bottom: "conv4_7/linear/bn"
  bottom: "conv5_1/linear/bn"
  top: "block_5_1"
}
layer {
  name: "conv5_2/expand"
  type: "Convolution"
  bottom: "block_5_1"
  top: "conv5_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv5_2/expand"
  top: "conv5_2/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_2/expand/scale"
  type: "Scale"
  bottom: "conv5_2/expand/bn"
  top: "conv5_2/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu5_2/expand"
  type: "ReLU"
  bottom: "conv5_2/expand/bn"
  top: "conv5_2/expand/bn"
}
layer {
  name: "conv5_2/dwise"
  type: "Convolution"
  bottom: "conv5_2/expand/bn"
  top: "conv5_2/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv5_2/dwise/bn"
  type: "BatchNorm"
  bottom: "conv5_2/dwise"
  top: "conv5_2/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_2/dwise/scale"
  type: "Scale"
  bottom: "conv5_2/dwise/bn"
  top: "conv5_2/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu5_2/dwise"
  type: "ReLU"
  bottom: "conv5_2/dwise/bn"
  top: "conv5_2/dwise/bn"
}
layer {
  name: "conv5_2/linear"
  type: "Convolution"
  bottom: "conv5_2/dwise/bn"
  top: "conv5_2/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2/linear/bn"
  type: "BatchNorm"
  bottom: "conv5_2/linear"
  top: "conv5_2/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_2/linear/scale"
  type: "Scale"
  bottom: "conv5_2/linear/bn"
  top: "conv5_2/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_5_2"
  type: "Eltwise"
  bottom: "block_5_1"
  bottom: "conv5_2/linear/bn"
  top: "block_5_2"
}
layer {
  name: "conv5_3/expand"
  type: "Convolution"
  bottom: "block_5_2"
  top: "conv5_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3/expand/bn"
  type: "BatchNorm"
  bottom: "conv5_3/expand"
  top: "conv5_3/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_3/expand/scale"
  type: "Scale"
  bottom: "conv5_3/expand/bn"
  top: "conv5_3/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu5_3/expand"
  type: "ReLU"
  bottom: "conv5_3/expand/bn"
  top: "conv5_3/expand/bn"
}
layer {
  name: "conv5_3/dwise"
  type: "Convolution"
  bottom: "conv5_3/expand/bn"
  top: "conv5_3/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    stride: 2
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv5_3/dwise/bn"
  type: "BatchNorm"
  bottom: "conv5_3/dwise"
  top: "conv5_3/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_3/dwise/scale"
  type: "Scale"
  bottom: "conv5_3/dwise/bn"
  top: "conv5_3/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu5_3/dwise"
  type: "ReLU"
  bottom: "conv5_3/dwise/bn"
  top: "conv5_3/dwise/bn"
}
layer {
  name: "conv5_3/linear"
  type: "Convolution"
  bottom: "conv5_3/dwise/bn"
  top: "conv5_3/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 160
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3/linear/bn"
  type: "BatchNorm"
  bottom: "conv5_3/linear"
  top: "conv5_3/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv5_3/linear/scale"
  type: "Scale"
  bottom: "conv5_3/linear/bn"
  top: "conv5_3/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv6_1/expand"
  type: "Convolution"
  bottom: "conv5_3/linear/bn"
  top: "conv6_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv6_1/expand"
  top: "conv6_1/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_1/expand/scale"
  type: "Scale"
  bottom: "conv6_1/expand/bn"
  top: "conv6_1/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu6_1/expand"
  type: "ReLU"
  bottom: "conv6_1/expand/bn"
  top: "conv6_1/expand/bn"
}
layer {
  name: "conv6_1/dwise"
  type: "Convolution"
  bottom: "conv6_1/expand/bn"
  top: "conv6_1/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 960
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv6_1/dwise/bn"
  type: "BatchNorm"
  bottom: "conv6_1/dwise"
  top: "conv6_1/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_1/dwise/scale"
  type: "Scale"
  bottom: "conv6_1/dwise/bn"
  top: "conv6_1/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu6_1/dwise"
  type: "ReLU"
  bottom: "conv6_1/dwise/bn"
  top: "conv6_1/dwise/bn"
}
layer {
  name: "conv6_1/linear"
  type: "Convolution"
  bottom: "conv6_1/dwise/bn"
  top: "conv6_1/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 160
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_1/linear/bn"
  type: "BatchNorm"
  bottom: "conv6_1/linear"
  top: "conv6_1/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_1/linear/scale"
  type: "Scale"
  bottom: "conv6_1/linear/bn"
  top: "conv6_1/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_6_1"
  type: "Eltwise"
  bottom: "conv5_3/linear/bn"
  bottom: "conv6_1/linear/bn"
  top: "block_6_1"
}
layer {
  name: "conv6_2/expand"
  type: "Convolution"
  bottom: "block_6_1"
  top: "conv6_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv6_2/expand"
  top: "conv6_2/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_2/expand/scale"
  type: "Scale"
  bottom: "conv6_2/expand/bn"
  top: "conv6_2/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu6_2/expand"
  type: "ReLU"
  bottom: "conv6_2/expand/bn"
  top: "conv6_2/expand/bn"
}
layer {
  name: "conv6_2/dwise"
  type: "Convolution"
  bottom: "conv6_2/expand/bn"
  top: "conv6_2/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 960
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv6_2/dwise/bn"
  type: "BatchNorm"
  bottom: "conv6_2/dwise"
  top: "conv6_2/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_2/dwise/scale"
  type: "Scale"
  bottom: "conv6_2/dwise/bn"
  top: "conv6_2/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu6_2/dwise"
  type: "ReLU"
  bottom: "conv6_2/dwise/bn"
  top: "conv6_2/dwise/bn"
}
layer {
  name: "conv6_2/linear"
  type: "Convolution"
  bottom: "conv6_2/dwise/bn"
  top: "conv6_2/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 160
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_2/linear/bn"
  type: "BatchNorm"
  bottom: "conv6_2/linear"
  top: "conv6_2/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_2/linear/scale"
  type: "Scale"
  bottom: "conv6_2/linear/bn"
  top: "conv6_2/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "block_6_2"
  type: "Eltwise"
  bottom: "block_6_1"
  bottom: "conv6_2/linear/bn"
  top: "block_6_2"
}
layer {
  name: "conv6_3/expand"
  type: "Convolution"
  bottom: "block_6_2"
  top: "conv6_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_3/expand/bn"
  type: "BatchNorm"
  bottom: "conv6_3/expand"
  top: "conv6_3/expand/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_3/expand/scale"
  type: "Scale"
  bottom: "conv6_3/expand/bn"
  top: "conv6_3/expand/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu6_3/expand"
  type: "ReLU"
  bottom: "conv6_3/expand/bn"
  top: "conv6_3/expand/bn"
}
layer {
  name: "conv6_3/dwise"
  type: "Convolution"
  bottom: "conv6_3/expand/bn"
  top: "conv6_3/dwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 960
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 960
    weight_filler {
      type: "msra"
    }
    engine: CAFFE
  }
}
layer {
  name: "conv6_3/dwise/bn"
  type: "BatchNorm"
  bottom: "conv6_3/dwise"
  top: "conv6_3/dwise/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_3/dwise/scale"
  type: "Scale"
  bottom: "conv6_3/dwise/bn"
  top: "conv6_3/dwise/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "relu6_3/dwise"
  type: "ReLU"
  bottom: "conv6_3/dwise/bn"
  top: "conv6_3/dwise/bn"
}
layer {
  name: "conv6_3/linear"
  type: "Convolution"
  bottom: "conv6_3/dwise/bn"
  top: "conv6_3/linear"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 320
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_3/linear/bn"
  type: "BatchNorm"
  bottom: "conv6_3/linear"
  top: "conv6_3/linear/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_3/linear/scale"
  type: "Scale"
  bottom: "conv6_3/linear/bn"
  top: "conv6_3/linear/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.000000001
  }
}
layer {
  name: "conv6_4"
  type: "Convolution"
  bottom: "conv6_3/linear/bn"
  top: "conv6_4"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1280
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6_4/bn"
  type: "BatchNorm"
  bottom: "conv6_4"
  top: "conv6_4/bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
}
layer {
  name: "conv6_4/scale"
  type: "Scale"
  bottom: "conv6_4/bn"
  top: "conv6_4/bn"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 0.5
    }
    bias_term: true
    bias_filler {
      value: 0
    }
    l1_lambda: 0.001
  }
}
layer {
  name: "relu6_4"
  type: "ReLU"
  bottom: "conv6_4/bn"
  top: "conv6_4/bn"
}
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6_4/bn"
  top: "pool6"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "food_fc7"
  type: "Convolution"
  bottom: "pool6"
  top: "fc7"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    #num_output: 143
    num_output: 43
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc7"
  bottom: "label"
  top: "loss"
}
layer {
  name: "top1/acc"
  type: "Accuracy"
  bottom: "fc7"
  bottom: "label"
  top: "top1/acc"
  include {
    phase: TEST
  }
}
layer {
  name: "top5/acc"
  type: "Accuracy"
  bottom: "fc7"
  bottom: "label"
  top: "top5/acc"
  include {
    phase: TEST
  }
  accuracy_param {
    top_k: 5
  }
}
posted on 2019-08-08 18:07 Sanny.Liu-CV&&ML 阅读(736) 评论(0) 编辑收藏举报
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MobileNet V2深入理解

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